class_docstr: Converts raw neural network output into a forecast and computes loss.
class_docstr_tok: ["Converts","raw","neural","network","output","into","a","forecast","and","computes","loss","."]
docstr: Converts arguments to the right shape and domain. The domain depends
  on the type of distribution, while the correct shape is obtained by
  reshaping the trailing axis in such a way that the returned tensors
  define a distribution of the right event_shape.
docstr_tok: ["Converts","arguments","to","the","right","shape","and","domain",".","The","domain","depends","on","the","type","of","distribution",",","while","the","correct","shape","is","obtained","by","reshaping","the","trailing","axis","in","such","a","way","that","the","returned","tensors","define","a","distribution","of","the","right","event_shape","."]
code: def domain_map(self, *args: torch.Tensor) -> Tuple[torch.Tensor, ...]:
      r"""
      Converts arguments to the right shape and domain. The domain depends
      on the type of distribution, while the correct shape is obtained by
      reshaping the trailing axis in such a way that the returned tensors
      define a distribution of the right event_shape.
      """
      raise NotImplementedError()
  
file_path: gluonts/src/gluonts/torch/distributions/output.py
imports: from typing import Callable, Dict, Optional, Tuple, Type
  import numpy
  import torch
  import torch.nn
  from gluonts.model.forecast_generator import ForecastGenerator
  from gluonts.torch.modules.lambda_layer import LambdaLayer
label_desc: Use image node_id 6 for calling the Output obj's underlying member method code with example usage: obj.domain_map() without return types